Predicting Likely Receivers Throughout an NFL Play

Zachary Pipping, Lou Zhou | Karim Kassam

Motivation

Determining who the likely recipient can be key in making the optimal decision for the defene as well as evaluating the decision-making of a quarterback
- Rewarding quarterbacks for finding uncommon but high-value passes
- For a defense, determining likely throw target for appropriate positioning

Look to build a ranking model which determines the most likely receiver at a frame given throw attempt

Data Overview

2025 NFL Big Data Bowl – Weeks 1–9

Game Data – Home and Away Team, Final Score, Game Time

Play Data – Play Description, Game Context, Play Result, Changes in Win Probability

Player Play Data – Statistics for each player for a play
- Route ran by player, Whether the player made a tackle or interception

Tracking Data - Locations of players and the football at each frame of a play

Spacing Tells an Incomplete Story

Methodology

Building a ranking algorithm(e.g. XGBoost) to rank the likeliest recipient at a frame
- Similar approaches in soccer¹
- Extracting features from tracking and play-by-play data

Potential pre-snap work, predicting the most likely target given the receiver alignments

Next Steps

Extracting features used to build the ranking model
- Distance, Relative Speed, Relative Orientation From Nearest Defender
- Quarterback Position, If Under Pressure
- Number of Defenders between Quarterback and Receiver, Passing Angle
- Game Score, Time Remaining

Building baseline models to compare with ranking algorithm